Risk Factor Forecasting System for Pressure Injuries Through Artificial Neural Network
The appearance of pressure injuries is very common in patients bedridden for a long time due to a surgical procedure or a recovery process caused by an accident. Many studies have been carried out to monitor and prevent this condition, but the methodology used requires the direct or indirect interve...
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| Veröffentlicht in: | Revista IEEE América Latina 2022-04, Vol.20 (4), p.634-642 |
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| creator | Medina Pedroso, Bruno Sartori Guazzelli, Joao Vitor Pereira da Silva, Alessandro Matos da Silva Boschi, Silvia Regina Cristina Martini, Silvia Augusto Scardovelli, Terigi |
| description | The appearance of pressure injuries is very common in patients bedridden for a long time due to a surgical procedure or a recovery process caused by an accident. Many studies have been carried out to monitor and prevent this condition, but the methodology used requires the direct or indirect intervention of a health professional to classify the risk of wound development. This study aims to demonstrate the development of a system capable of predicting the risk factor for the development of pressure injuries through the analysis of the Braden Scale parameters inserted by a health professional on an electronic interface, where through an algorithm based on artificial neural networks, which is responsible for processing, it will be possible to carry out the classification of the risk factor for pressure injuries. To acquire the friction and shear parameters, force sensors were used in a matrix architecture, together with a signal conditioning circuit as well as a control and communication drive via USB with the computer for sending data, as well as a graphical interface for entry of other parameters by the health professional. |
| doi_str_mv | 10.1109/TLA.2022.9675469 |
| format | Article |
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Many studies have been carried out to monitor and prevent this condition, but the methodology used requires the direct or indirect intervention of a health professional to classify the risk of wound development. This study aims to demonstrate the development of a system capable of predicting the risk factor for the development of pressure injuries through the analysis of the Braden Scale parameters inserted by a health professional on an electronic interface, where through an algorithm based on artificial neural networks, which is responsible for processing, it will be possible to carry out the classification of the risk factor for pressure injuries. 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(IEEE) 2022</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><orcidid>0000-0003-3442-9932 ; 0000-0002-6523-9376 ; 0000-0001-8125-8844 ; 0000-0003-4766-8617 ; 0000-0003-0378-6056 ; 0000-0002-3430-8885</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9675469$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9675469$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Medina Pedroso, Bruno</creatorcontrib><creatorcontrib>Sartori Guazzelli, Joao Vitor</creatorcontrib><creatorcontrib>Pereira da Silva, Alessandro</creatorcontrib><creatorcontrib>Matos da Silva Boschi, Silvia Regina</creatorcontrib><creatorcontrib>Cristina Martini, Silvia</creatorcontrib><creatorcontrib>Augusto Scardovelli, Terigi</creatorcontrib><title>Risk Factor Forecasting System for Pressure Injuries Through Artificial Neural Network</title><title>Revista IEEE América Latina</title><addtitle>T-LA</addtitle><description>The appearance of pressure injuries is very common in patients bedridden for a long time due to a surgical procedure or a recovery process caused by an accident. 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| subjects | Algorithms Artificial Neural Network Artificial neural networks Biomedical monitoring Braden Scale Circuits Embedded Electronics Forecasting Injuries Injury analysis Manuals Monitoring Neural networks Parameters Pressure Injuries Risk analysis Risk factors RNA Thin film transistors |
| title | Risk Factor Forecasting System for Pressure Injuries Through Artificial Neural Network |
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